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All Journal BERKALA FISIKA Jurnal Fisika Unand
Qidir Maulana Binu Soesanto
Departemen Fisika, Fakultas Sains dan Matematika, Universitas Diponegoro
Earth & Planetary Sciences Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Physics

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REVIEW PEMBENTUKAN MASSA NEUTRINO MELALUI MEKANISME SEESAW TIPE II Qidir Maulana Binu Soesanto; Ketut Sofjan Firdausi
BERKALA FISIKA Vol 26, No 2 (2023): Berkala Fisika
Publisher : BERKALA FISIKA

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Abstract

Mekanisme Seesaw adalah salah satu mekanisme untuk memunculkan massa neutrino yang tidak tercakup  dalam  Model  Standar  fisika  partikel. Terdapat  tiga  tipe  pada  mekanisme  Seesaw,  yaitu tipe I, II, dan III. Dalam tulisan ini, kami membahas mengenai mekanime Seesaw tipe II. Pada tipe ini,  tiga  skalar  triplet  diperkenalkan.  Dengan  adanya  triplet  skalar  dan  nilai  harap  vakum  dari medan skalar baru ini, kami akan menjelaskan bahwa massa neutrino bisa terbentuk.
Klasifikasi Multi-Kelas Kanker Paru Berbasis Ekstraksi Fitur Hibrida GLCM, LBP dan Gabor Filter Menggunakan Algoritma Random Forest dan KNN pada Citra CT-Scan Muhammad Iqbal; Pandji Triadyaksa; Qidir Maulana Binu Soesanto
Jurnal Fisika Unand Vol 15 No 1 (2026)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jfu.15.1.57-69.2026

Abstract

This study aims to improve the multi-class classification performance of Non-Small Cell Lung Cancer (NSCLC) based on CT-Scan images through a hybrid feature extraction approach. The method combines Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Gabor Filter features, classified using Random Forest and K-Nearest Neighbor (KNN) algorithms. The dataset includes four image classes: adenocarcinoma, large cell carcinoma, squamous cell carcinoma, and normal. Model performance was evaluated using accuracy, sensitivity, specificity, and area under curve (AUC) metrics. The results show that the GLCM+LBP feature combination with the Random Forest algorithm achieved the best performance with an accuracy of 99.22%. The model effectively recognizes both global and local texture variations of lung tissue and remains stable in distinguishing all image classes. It can be concluded that the hybrid texture feature combination and ensemble algorithm produce an accurate, efficient, and potentially applicable classification model for computer-aided diagnosis in medical physics
Co-Authors Ketut Sofjan Firdausi Muhammad Iqbal Pandji Triadyaksa